- Risk of post-acute sequelae of SARS-CoV-2 infection associated with pre-coronavirus disease obstructive sleep apnea diagnoses: an electronic health record-based analysis from the RECOVER initiative.
- Obstructive sleep apnea (OSA) has been associated with more severe acute coronavirus disease-2019 (COVID-19) outcomes. Researchers assessed the impact of preexisting OSA on the risk for probable PASC in adults and children using electronic health record data from multiple research networks. Three research networks within the REsearching COVID to Enhance Recovery initiative (PCORnet Adult, PCORnet Pediatric, and the National COVID Cohort Collaborative [N3C]) employed a harmonized analytic approach to examine the risk of probable PASC in COVID-19-positive patients with and without a diagnosis of OSA prior to pandemic onset. Across networks, the unadjusted OR for probable PASC associated with a preexisting OSA diagnosis in adults and children ranged from 1.41 to 3.93. Adjusted analyses found an attenuated association that remained significant among adults only. Multiple sensitivity analyses with expanded inclusion criteria and covariates yielded results consistent with the primary analysis. Adults with preexisting OSA were found to have significantly elevated odds of probable PASC. This finding was consistent across data sources, approaches for identifying COVID-19-positive patients, and definitions of PASC. Patients with OSA may be at elevated risk for PASC after SARS-CoV-2 infection and should be monitored for post-acute sequelae.
- Trajectories of the evolution of post COVID-19 condition, up to two years after symptoms onset.
- Researchers aimed to identify trajectories of the evolution of post COVID-19 condition, up to two years after symptom onset. The ComPaRe long COVID e-cohort is a prospective cohort of patients with symptoms lasting at least two months after SARS-CoV2 infection. Researchers used trajectory modelling to identify different trajectories in the evolution of post COVID-19 condition, based on symptoms collected every 60 days using the long COVID Symptom Tool. A total of 2,197 patients were enrolled in the cohort between December 2020 and July 2022 when the Omicron variant was not dominant. Three trajectories of the evolution of post COVID-19 condition were identified: “high persistent symptoms” (4%), “rapidly decreasing symptoms” (5%), and “slowly decreasing symptoms” (91%). Participants with high persistent symptoms were older and more likely to report a history of systemic diseases. They often reported tachycardia, bradycardia, palpitations, and arrhythmia. Participants with rapidly decreasing symptoms were younger and more likely to report a confirmed infection. They often reported diarrhoea and back pain. Participants with slowly decreasing symptoms were more likely to have functional diseases. Most of patients with post COVID-19 condition improve slowly over time, while 5% have rapid improvement in the two years after symptom onset and 4% have a persistent condition.
- Virus variant specific clinical performance of a SARS-CoV-2 rapid antigen test with focus on omicron VOC.
- Antigen rapid diagnostic tests (Ag-RDTs) play an important role in the diagnosis of SARS-CoV-2. They are easier, quicker, and less expensive than the “gold standard” RT-PCR and therefore widely in use. Reliable clinical data with respect to Ag-RDT performance in SARS-CoV-2 Omicron VOCs is limited. Consequently, the objective of this study was to determine the impact different VOCs – especially Omicron – have on the clinical performance of an Ag-RDT. Researchers compared the clinical performance of the Sofia SARS-CoV-2 Ag-RDT to RT-PCR in a real-world, single-centre study in a clinical point-of-care setting in patients admitted to a large hospital via the emergency department from 2 November 2020 to 4 September 2022. Among 38,434 Ag-RDT/RT-PCR tandems taken, 1528 yielded a SARS-CoV-2 positive RT-PCR test result, with a prevalence of 4.0% (95% CI, 3.8 – 4.2). Overall sensitivity of the Ag-RDT was 63.7% (95% CI, 61.3 – 66.1) and overall specificity was 99.6% (95% CI, 99.5 – 99.6). Ag-RDT sensitivity was dependent on viral load (VL), as the sensitivity increased to 93.2% (95% CI, 91.5 – 94.6) in samples with a VL >106 SARS-CoV-2 copies/ml. Furthermore, the Ag-RDT was more sensitive in men, and older patients. Variant-dependent sensitivity assessment showed that the sensitivity was significantly lower in Omicron-VOC (64.1%; 95% CI, 60.5 – 67.6) compared to SARS-CoV-2 Wild-type samples (70.0%; 95% CI, 59,8 – 78,6) (binomial test; p-value <0.001). Analyzing the limits of detection (LoD) showed a 27 times higher 95% LoD for the Omicron-VOC BA.5 compared to the SARS-CoV-2 Wild-type.Ag-RDT sensitivity for detection of patients with lower viral loads and with Omicron-VOC is reduced, limiting the effectiveness of Ag-RDTs. However, Ag-RDTs are still an unreplaceable tool for widely available, quick, and inexpensive point-of-care SARS-CoV-2 diagnostics.
- Ventilation in Buildings
- Airborne viral particles spread between people more readily indoors than outdoors. Indoors, the concentration of viral particles is often higher than outdoors. Protective indoor ventilation practices can reduce the airborne viral concentrations and the overall viral exposure to occupants. Ventilation system upgrades or improvements can increase the delivery of clean air and dilute potential contaminants. Buildings that provided healthy, code-compliant indoor air quality prior to the pandemic can be improved for pandemic and post-pandemic occupancy using less costly interventions. While the mitigation strategies can be universally applied across many indoor environments, applying them to different building types, occupancies, and activities under environmental and seasonal changes can be challenging. The building owner or operator should identify which strategies are appropriate for each building throughout the year. Implementing multiple building-level mitigation strategies at the same time is consistent with CDC’s layered approach and will increase overall effectiveness of ventilation interventions.
- COVID-19 mortality among selective serotonin reuptake inhibitor users - Results from a nationwide cohort.
- To examine differences in mortality and/or severe acute respiratory syndrome between selective serotonin reuptake inhibitor- (SSRI) users and non-SSRI-users up to 60 days after a positive severe-acute-respiratory-syndrome-coronavirus-2 (SARS-CoV-2) real-time reverse transcription-polymerase chain reaction (PCR) test. Retrospective cohort study including all Danish residents above the age of eighteen with a positive SARS- CoV-2 PCR test from February 26th, 2020, to October 5th2021. The follow-up period was 60 days. The primary outcome was all-cause mortality, and the secondary outcome was severe acute respiratory syndrome. Exposure of interest was SSRI-use. Differences between SSRI- users and non-users were examined with Cox Regression. 286447 SARS-CoV-2 positive individuals were identified, and 7113 met the criteria for SSRI-use. SSRI-users had a mean age of 50.4 years, and 34% were male. Non-SSRI users had a mean age of 41.4 years, and 50 % were male. Similar vaccination frequency was seen among the two groups. Sertraline was the most commonly used SSRI, followed by citalopram and escitalopram. Researchers found 255 deaths among SSRI users (3.6%) and 2872 deaths among non-SSRI users (1.0%). SSRI-use was significantly associated with increased mortality, with a hazard ratio of 1.32 (95% CI [1.16 -1.50], p=0.015), even when adjusting for age, sex, vaccination status and comorbidities. Researchers found significantly higher mortality when comparing SSRI-users to non-SSRI-users within60 days after a positive SARS-CoV-2 PCR test. Even when considering possible residual confounding positive effect of SSRI-intake seems highly unlikely. This study therefore speaks against the hypothesis of repurposing SSRI-drugs for COVID-19 treatment.
- Efficacy of cognitive behavioral therapy targeting severe fatigue following COVID-19: results of a randomized controlled trial.
- Severe fatigue following COVID-19 is prevalent and debilitating. This study investigated the efficacy of cognitive behavioral therapy (CBT) for severe fatigue following COVID-19. A multicenter, 2-arm randomized controlled trial was conducted in the Netherlands with patients being severely fatigued 3-12 months following COVID-19. Patients (n = 114) were randomly assigned (1:1) to CBT or care as usual (CAU). CBT, targeting perpetuating factors of fatigue, was provided for 17 weeks. The primary outcome was the overall mean difference between CBT and CAU on the fatigue severity subscale of the Checklist Individual Strength, directly post CBT or CAU (T1), and after six months (T2). Secondary outcomes were differences in proportions of patients meeting criteria for severe and/or chronic fatigue, differences in physical and social functioning, somatic symptoms and problems concentrating between CBT and CAU. Patients were mainly non-hospitalized and self-referred. Patients who received CBT were significantly less severely fatigued across follow-up assessments than patients receiving CAU (-8.8, (95% confidence interval (CI)) -11.9 to -5.8); P < 0.001), representing a medium Cohen’s d effect size (0.69). The between-group difference in fatigue severity was present at T1 -9.3 (95% CI -13.3 to -5.3) and T2 -8.4 (95% CI -13.1 to -3.7). All secondary outcomes favored CBT. Eight adverse events were recorded during CBT, and 20 during CAU. No serious adverse events were recorded. Among patients, who were mainly non-hospitalized and self-referred, CBT was effective in reducing fatigue. The positive effect was sustained at six-month follow-up.
World Health Organization (WHO)
Novel Coronavirus (COVID-19) Situation from World Health Organization (WHO)
Johns Hopkins University (JHU)
Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at JHU
COVID-19 in US and Canada
1Point3Acres Real-Time Coronavirus (COVID-19) Updates in US and Canada with Credible Sources
Genomic Epidemiology COVID-19
Genomic Epidemiology of (COVID-19) Maintained by the Nextstrain team, enabled by data from GISAID.